Multi-Segment State of Health Estimation of Lithium-ion Batteries Considering Short Partial Charging
Bibliographic record
Abstract
State of health (SOH) is a critical state parameter of lithium-ion batteries (LIBs). Health indicators (HIs), which are derived from the measured features of LIBs, are used in the current data-driven SOH estimation techniques to determine SOH. However, the common partial charging and discharging make it challenging to derive reliable HIs. In this paper, a SOH estimation approach considering short partial charging is proposed. Unlike other techniques, the constant current charging stage is divided into short segments, the HI, based on the charging capacity and actual initial charging voltage, is extracted within each short segment, and a kernel ridge regression-based estimator is created to characterize the SOH mapping relationship. Subsequently, an estimator fusion frame is established to merge the estimates of the eligible segments, which is decided based on the actual start and end charging voltages of the partial charging. The effectiveness of the proposed approach is validated with two well-known LIBs aging datasets containing real partial charging cycles. The results are satisfactory in terms of accuracy, robustness to partial charging, and good generality to different types of LIBs. Effective SOH value can be deduced whenever the charging voltage range covers at least one short estimation segment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".